





import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

import pyarrow as pa
import pyarrow.parquet as pq
import pytest

import datasets
import datasets.config





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       )							-> Optional[Any]:




    """simple docstring"""




    _A     =							10
    _A     =							datasets.Features(
        {
            'tokens': datasets.Sequence(datasets.Value('string'				)				),
            'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive']				)				),
            'answers': datasets.Sequence(
                {
                    'text': datasets.Value('string'				),
                    'answer_start': datasets.Value('int32'				),
                }				),
            'id': datasets.Value('int64'				),
        }				)
    _A     =							datasets.Dataset.from_dict(
        {
            'tokens': [['foo'] * 5] * n,
            'labels': [[1] * 5] * n,
            'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
            'id': list(range(_SCREAMING_SNAKE_CASE				)				),
        }							,     features=_SCREAMING_SNAKE_CASE							,     )
    return dataset





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'file.arrow'				)
    dataset.map(cache_file_name=_SCREAMING_SNAKE_CASE				)
    return filename


# FILE_CONTENT + files


__A							:			Union[str, Any]         =			"\\n    Text data.\n    Second line of data."





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'file.txt'
    _A     =							FILE_CONTENT
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE				)
    return filename





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    import bza

    _A     =							tmp_path_factory.mktemp('data'				) / 'file.txt.bz2'
    _A     =							bytes(_SCREAMING_SNAKE_CASE							,     'utf-8'				)
    with bza.open(_SCREAMING_SNAKE_CASE							,     'wb'				) as f:
        f.write(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    import gzip

    _A     =							str(tmp_path_factory.mktemp('data'				) / 'file.txt.gz'				)
    _A     =							bytes(_SCREAMING_SNAKE_CASE							,     'utf-8'				)
    with gzip.open(_SCREAMING_SNAKE_CASE							,     'wb'				) as f:
        f.write(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    if datasets.config.LZ4_AVAILABLE:
        import lza.frame

        _A     =							tmp_path_factory.mktemp('data'				) / 'file.txt.lz4'
        _A     =							bytes(_SCREAMING_SNAKE_CASE							,     'utf-8'				)
        with lza.frame.open(_SCREAMING_SNAKE_CASE							,     'wb'				) as f:
            f.write(_SCREAMING_SNAKE_CASE				)
        return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    if datasets.config.PY7ZR_AVAILABLE:
        import pyazr

        _A     =							tmp_path_factory.mktemp('data'				) / 'file.txt.7z'
        with pyazr.SevenZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as archive:
            archive.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
        return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    import tarfile

    _A     =							tmp_path_factory.mktemp('data'				) / 'file.txt.tar'
    with tarfile.TarFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.add(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    import lzma

    _A     =							tmp_path_factory.mktemp('data'				) / 'file.txt.xz'
    _A     =							bytes(_SCREAMING_SNAKE_CASE							,     'utf-8'				)
    with lzma.open(_SCREAMING_SNAKE_CASE							,     'wb'				) as f:
        f.write(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    import zipfile

    _A     =							tmp_path_factory.mktemp('data'				) / 'file.txt.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    if datasets.config.ZSTANDARD_AVAILABLE:
        import zstandard as zstd

        _A     =							tmp_path_factory.mktemp('data'				) / 'file.txt.zst'
        _A     =							bytes(_SCREAMING_SNAKE_CASE							,     'utf-8'				)
        with zstd.open(_SCREAMING_SNAKE_CASE							,     'wb'				) as f:
            f.write(_SCREAMING_SNAKE_CASE				)
        return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'file.xml'
    _A     =							textwrap.dedent(
        '\\n    <?xml version="1.0" encoding="UTF-8" ?>\n    <tmx version="1.4">\n      <header segtype="sentence" srclang="ca" />\n      <body>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n        </tu>\n      </body>\n    </tmx>'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE				)
    return filename


__A							:			Optional[int]         =			[
    {"col_1": "0", "col_2": 0, "col_3": 0.0},
    {"col_1": "1", "col_2": 1, "col_3": 1.0},
    {"col_1": "2", "col_2": 2, "col_3": 2.0},
    {"col_1": "3", "col_2": 3, "col_3": 3.0},
]
__A							:			Optional[Any]         =			[
    {"col_1": "4", "col_2": 4, "col_3": 4.0},
    {"col_1": "5", "col_2": 5, "col_3": 5.0},
]
__A							:			Any         =			{
    "col_1": ["0", "1", "2", "3"],
    "col_2": [0, 1, 2, 3],
    "col_3": [0.0, 1.0, 2.0, 3.0],
}

__A							:			Optional[Any]         =			[
    {"col_3": 0.0, "col_1": "0", "col_2": 0},
    {"col_3": 1.0, "col_1": "1", "col_2": 1},
]

__A							:			str         =			[
    {"col_1": "s0", "col_2": 0, "col_3": 0.0},
    {"col_1": "s1", "col_2": 1, "col_3": 1.0},
    {"col_1": "s2", "col_2": 2, "col_3": 2.0},
    {"col_1": "s3", "col_2": 3, "col_3": 3.0},
]





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       )							-> List[str]:




    """simple docstring"""




    return DATA_DICT_OF_LISTS





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    _A     =							datasets.Dataset.from_dict(_SCREAMING_SNAKE_CASE				)
    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.arrow'				)
    dataset.map(cache_file_name=_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.sqlite'				)
    with contextlib.closing(sqlitea.connect(_SCREAMING_SNAKE_CASE				)				) as con:
        _A     =							con.cursor()
        cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)'				)
        for item in DATA:
            cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)'							,     tuple(item.values()				)				)
        con.commit()
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.csv'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'							,     newline=''				) as f:
        _A     =							csv.DictWriter(_SCREAMING_SNAKE_CASE							,     fieldnames=['col_1', 'col_2', 'col_3']				)
        writer.writeheader()
        for item in DATA:
            writer.writerow(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset2.csv'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'							,     newline=''				) as f:
        _A     =							csv.DictWriter(_SCREAMING_SNAKE_CASE							,     fieldnames=['col_1', 'col_2', 'col_3']				)
        writer.writeheader()
        for item in DATA:
            writer.writerow(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    import bza

    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.csv.bz2'
    with open(_SCREAMING_SNAKE_CASE							,     'rb'				) as f:
        _A     =							f.read()
    # data = bytes(FILE_CONTENT, "utf-8")
    with bza.open(_SCREAMING_SNAKE_CASE							,     'wb'				) as f:
        f.write(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.csv.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.csv.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(csv_path.replace('.csv'							,     '.CSV'				)				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(csva_path.replace('.csv'							,     '.CSV'				)				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.csv.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.join('main_dir'							,     os.path.basename(_SCREAMING_SNAKE_CASE				)				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.join('main_dir'							,     os.path.basename(_SCREAMING_SNAKE_CASE				)				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.parquet'				)
    _A     =							pa.schema(
        {
            'col_1': pa.string(),
            'col_2': pa.intaa(),
            'col_3': pa.floataa(),
        }				)
    with open(_SCREAMING_SNAKE_CASE							,     'wb'				) as f:
        _A     =							pq.ParquetWriter(_SCREAMING_SNAKE_CASE							,     schema=_SCREAMING_SNAKE_CASE				)
        _A     =							pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_SCREAMING_SNAKE_CASE				)				)] for k in DATA[0]}							,     schema=_SCREAMING_SNAKE_CASE				)
        writer.write_table(_SCREAMING_SNAKE_CASE				)
        writer.close()
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.json'				)
    _A     =							{'data': DATA}
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        json.dump(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.json'				)
    _A     =							{'data': DATA_DICT_OF_LISTS}
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        json.dump(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.jsonl'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        for item in DATA:
            f.write(json.dumps(_SCREAMING_SNAKE_CASE				) + '\n'				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset2.jsonl'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        for item in DATA:
            f.write(json.dumps(_SCREAMING_SNAKE_CASE				) + '\n'				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset_312.jsonl'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        for item in DATA_312:
            f.write(json.dumps(_SCREAMING_SNAKE_CASE				) + '\n'				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset-str.jsonl'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        for item in DATA_STR:
            f.write(json.dumps(_SCREAMING_SNAKE_CASE				) + '\n'				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    import gzip

    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.txt.gz'				)
    with open(_SCREAMING_SNAKE_CASE							,     'rb'				) as orig_file:
        with gzip.open(_SCREAMING_SNAKE_CASE							,     'wb'				) as zipped_file:
            zipped_file.writelines(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    import gzip

    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.jsonl.gz'				)
    with open(_SCREAMING_SNAKE_CASE							,     'rb'				) as orig_file:
        with gzip.open(_SCREAMING_SNAKE_CASE							,     'wb'				) as zipped_file:
            zipped_file.writelines(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.jsonl.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset_nested.jsonl.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.join('nested'							,     os.path.basename(_SCREAMING_SNAKE_CASE				)				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.jsonl.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.join('main_dir'							,     os.path.basename(_SCREAMING_SNAKE_CASE				)				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.join('main_dir'							,     os.path.basename(_SCREAMING_SNAKE_CASE				)				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.jsonl.tar'
    with tarfile.TarFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.add(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
        f.add(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset_nested.jsonl.tar'
    with tarfile.TarFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.add(_SCREAMING_SNAKE_CASE							,     arcname=os.path.join('nested'							,     os.path.basename(_SCREAMING_SNAKE_CASE				)				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							['0', '1', '2', '3']
    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset.txt'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        for item in data:
            f.write(item + '\n'				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							['0', '1', '2', '3']
    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset2.txt'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        for item in data:
            f.write(item + '\n'				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							['0', '1', '2', '3']
    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.abc'
    with open(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        for item in data:
            f.write(item + '\n'				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.text.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.text.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.join('main_dir'							,     os.path.basename(_SCREAMING_SNAKE_CASE				)				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.join('main_dir'							,     os.path.basename(_SCREAMING_SNAKE_CASE				)				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.ext.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename('unsupported.ext'				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename('unsupported_2.ext'				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							'\n'.join(['First', 'Second\u2029with Unicode new line', 'Third']				)
    _A     =							str(tmp_path_factory.mktemp('data'				) / 'dataset_with_unicode_new_lines.txt'				)
    with open(_SCREAMING_SNAKE_CASE							,     'w'							,     encoding='utf-8'				) as f:
        f.write(_SCREAMING_SNAKE_CASE				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       )							-> Dict:




    """simple docstring"""




    return os.path.join('tests'							,     'features'							,     'data'							,     'test_image_rgb.jpg'				)





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       )							-> Union[str, Any]:




    """simple docstring"""




    return os.path.join('tests'							,     'features'							,     'data'							,     'test_audio_44100.wav'				)





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data'				) / 'dataset.img.zip'
    with zipfile.ZipFile(_SCREAMING_SNAKE_CASE							,     'w'				) as f:
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				)				)
        f.write(_SCREAMING_SNAKE_CASE							,     arcname=os.path.basename(_SCREAMING_SNAKE_CASE				).replace('.jpg'							,     '2.jpg'				)				)
    return path





@pytest.fixture(scope='session'				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							tmp_path_factory.mktemp('data_dir'				)

    (data_dir / "subdir").mkdir()
    with open(data_dir / 'subdir' / 'train.txt'							,     'w'				) as f:
        f.write('foo\n' * 10				)
    with open(data_dir / 'subdir' / 'test.txt'							,     'w'				) as f:
        f.write('bar\n' * 10				)
    # hidden file
    with open(data_dir / 'subdir' / '.test.txt'							,     'w'				) as f:
        f.write('bar\n' * 10				)

    # hidden directory
    (data_dir / ".subdir").mkdir()
    with open(data_dir / '.subdir' / 'train.txt'							,     'w'				) as f:
        f.write('foo\n' * 10				)
    with open(data_dir / '.subdir' / 'test.txt'							,     'w'				) as f:
        f.write('bar\n' * 10				)

    return data_dir









"""simple docstring"""



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)

lowercase_							=							'Hello, World!'
lowercase_							=							'en_XX'
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   bool	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			Path('''data_bin'''	)
	lowerCAmelCase_      							=			FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowercase	).parent	)			,   checkpoint_file=Path(_lowercase	).name			,   _name='''xmod_base'''			,   arch='''xmod_base'''			,   task='''multilingual_masked_lm'''			,   data_name_or_path=str(_lowercase	)			,   bpe='''sentencepiece'''			,   sentencepiece_model=str(Path(_lowercase	).parent / '''sentencepiece.bpe.model'''	)			,   src_dict=str(data_dir / '''dict.txt'''	)			,   )
	xmod.eval()  # disable dropout
	print(_lowercase	)

	lowerCAmelCase_      							=			xmod.model.encoder.sentence_encoder
	lowerCAmelCase_      							=			XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,   hidden_size=xmod.cfg.model.encoder_embed_dim			,   num_hidden_layers=xmod.cfg.model.encoder_layers			,   num_attention_heads=xmod.cfg.model.encoder_attention_heads			,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,   max_position_embeddings=5_1_4			,   type_vocab_size=1			,   layer_norm_eps=1E-5			,   pre_norm=xmod.cfg.model.encoder_normalize_before			,   adapter_reduction_factor=getattr(xmod.cfg.model			,   '''bottleneck'''			,   2	)			,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,   ln_before_adapter=xmod.cfg.model.ln_before_adapter			,   languages=xmod.cfg.model.languages			,   )
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our X-MOD config:'''			,   _lowercase	)

	lowerCAmelCase_      							=			XmodForSequenceClassification(_lowercase	) if classification_head else XmodForMaskedLM(_lowercase	)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_tokens.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_positions.weight
	lowerCAmelCase_      							=			torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers	):
		# Encoder: start of layer
		lowerCAmelCase_      							=			model.roberta.encoder.layer[i]
		lowerCAmelCase_      							=			xmod_sent_encoder.layers[i]

		# self attention
		lowerCAmelCase_      							=			layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)	)
		):
			raise AssertionError('''Dimensions of self-attention weights do not match.'''	)

		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase_      							=			layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('''Dimensions of self-attention output weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.bias

		# intermediate
		lowerCAmelCase_      							=			layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of intermediate weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias

		# output
		lowerCAmelCase_      							=			layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of feed-forward weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.weight
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
			raise AssertionError('''Lists of language adapters do not match.'''	)
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			lowerCAmelCase_      							=			bert_output.adapter_modules[lang_code]
			lowerCAmelCase_      							=			xmod_layer.adapter_modules[lang_code]
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.weight
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.bias

	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase_      							=			xmod.encode(_lowercase	).unsqueeze(0	)  # batch of size 1
	model.roberta.set_default_language(_lowercase	)

	lowerCAmelCase_      							=			model(_lowercase	)[0]
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(_lowercase	)	)
	else:
		lowerCAmelCase_      							=			xmod.model(_lowercase			,   lang_id=[SAMPLE_LANGUAGE]	)[0]
	print(our_output.shape			,   their_output.shape	)
	lowerCAmelCase_      							=			torch.max(torch.abs(our_output - their_output	)	).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	lowerCAmelCase_      							=			torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)
	print('''Do both models output the same tensors?'''			,   '''🔥''' if success else '''💩'''	)
	if not success:
		raise Exception('''Something went wRoNg'''	)

	Path(_lowercase	).mkdir(parents=_lowercase			,   exist_ok=_lowercase	)
	print(F"""Saving model to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
		)
		lowercase_							=							parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)